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Large-scale group decision making problems exist widely in human being's daily life. In this paper, a new approach to large-scale multi-attribute group decision making with multi-granular unbalanced linguistic information is developed. First, an algorithm is proposed to represent the initial multi-granular unbalanced linguistic information of decision makers with the use of unbalanced linguistic distribution assessments. Based on the gain and loss of an unbalanced linguistic distribution assessment over another, the classical TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) method is then extended to derive a raking of alternatives for large-scale multi-attribute group decision making problems. Finally, an example for talent selection is used to demonstrate the feasibility of the proposed approach.